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https://github.com/microsoft/tora

ToRA is a series of Tool-integrated Reasoning LLM Agents designed to solve challenging mathematical reasoning problems by interacting with tools [ICLR'24].
https://github.com/microsoft/tora

autonomous-agents language-model llm mathematical-reasoning tool-learning

Last synced: 15 days ago
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ToRA is a series of Tool-integrated Reasoning LLM Agents designed to solve challenging mathematical reasoning problems by interacting with tools [ICLR'24].

Host: GitHub
URL: https://github.com/microsoft/tora
Owner: microsoft
License: mit
Created: 2023-09-27T16:58:12.000Z (about 1 year ago)
Default Branch: main
Last Pushed: 2024-02-22T09:46:48.000Z (9 months ago)
Last Synced: 2024-11-05T05:33:53.870Z (15 days ago)
Topics: autonomous-agents, language-model, llm, mathematical-reasoning, tool-learning
Language: Python
Homepage: https://microsoft.github.io/ToRA/
Size: 16.2 MB
Stars: 970
Watchers: 18
Forks: 72
Open Issues: 5
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Security: SECURITY.md

Awesome Lists containing this project

README

        







ToRA: A Tool-Integrated Reasoning Agent





![](https://img.shields.io/badge/Task-Mathematical%20Reasoning-orange)

![](https://img.shields.io/badge/Model-Released-blue)

![](https://img.shields.io/badge/Code%20License-MIT-green)




[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/tora-a-tool-integrated-reasoning-agent-for/math-word-problem-solving-on-math)](https://paperswithcode.com/sota/math-word-problem-solving-on-math?p=tora-a-tool-integrated-reasoning-agent-for)





  [🌐 Website] •

  [📜 Paper] •

  [🤗 HF Models] •

  [🐱 GitHub]

  

  


  [🐦 Twitter] •

  [💬 Reddit] •

  [🍀 Unofficial Blog]

  

  





Repo for "ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving" [ICLR'2024]





    

        


    Figure 1: Comparing ToRA with baselines on LLaMA-2 base models from 7B to 70B.



## 🔥 News

- [2023/10/08] 🔥🔥🔥 All ToRA models released at [🤗 HuggingFace](https://huggingface.co/llm-agents)!

- [2023/09/29] ToRA paper, repo, and website released.

## 💡 Introduction

ToRA is a series of Tool-integrated Reasoning Agents designed to solve challenging mathematical reasoning problems by interacting with tools, e.g., computation libraries and symbolic solvers. ToRA series seamlessly integrate natural language reasoning with the utilization of external tools, thereby amalgamating the analytical prowess of language and the computational efficiency of external tools.

| Model | Size | GSM8k | MATH | AVG@10 math tasks^† |

|---|---|---|---|---|

| GPT-4 | - | 92.0 | 42.5 | 78.3 |

| GPT-4 (PAL) | - | 94.2 | 51.8 | 86.4 |

| [ ToRA-7B](https://huggingface.co/llm-agents/tora-7b-v1.0) | 7B | 68.8 | 40.1 | 62.4|

| [ ToRA-Code-7B](https://huggingface.co/llm-agents/tora-code-7b-v1.0) | 7B | 72.6 | 44.6 | 66.5| 

| ToRA-Code-7B + self-consistency (k=50) | 7B | 76.8 | 52.5 | - |

| [ ToRA-13B](https://huggingface.co/llm-agents/tora-13b-v1.0) | 13B |  72.7 | 43.0 | 65.9|

| [ ToRA-Code-13B](https://huggingface.co/llm-agents/tora-code-13b-v1.0) | 13B | 75.8 | 48.1 | 71.3 |

| ToRA-Code-13B + self-consistency (k=50) | 13B | 80.4 | 55.1 | - |

| [ ToRA-Code-34B^*](https://huggingface.co/llm-agents/tora-code-34b-v1.0) | 34B | 80.7 | **51.0** | 74.8 |

| ToRA-Code-34B + self-consistency (k=50) | 34B | 85.1 | **60.0** | - |

| [ ToRA-70B](https://huggingface.co/llm-agents/tora-70b-v1.0) | 70B | **84.3** | 49.7 | **76.9** |

| ToRA-70B + self-consistency (k=50) | 70B | **88.3** | 56.9 | - |

- ^*ToRA-Code-34B is currently the first and only open-source model to achieve over 50% accuracy (pass@1) on the MATH dataset, which significantly outperforms GPT-4’s CoT result (51.0 vs. 42.5), and is competitive with GPT-4 solving problems with programs. By open-sourcing our codes and models, we hope more breakthroughs will come!

- ^†10 math tasks include GSM8k, MATH, GSM-Hard, SVAMP, TabMWP, ASDiv, SingleEQ, SingleOP, AddSub, and MultiArith.

### Tool-Integrated Reasoning



    

    


    Figure 2: A basic example of single-round tool interaction, which interleaves rationales with program-based tool use.



### ToRA Training Pipeline



    

    


    Figure 3: Training ToRA contains ① Imitation Learning, and ② output space shaping.



## 🚀 Quick Start

### ⚙️ Setup

We recommend using [Conda](https://docs.conda.io/projects/miniconda) to manage your environment. We use [vLLM](https://github.com/vllm-project/vllm) (0.1.4) to accelerate inference. Run the following commands to setup your environment:

```sh

git clone https://github.com/microsoft/ToRA.git && cd ToRA/src

conda create -n tora python=3.10

conda activate tora

pip install packaging==22.0

pip install torch==2.0.1 --index-url https://download.pytorch.org/whl/cu118 # CUDA 11.8 for example

pip install -r requirements.txt

```

### 🪁 Inference

We provide a script for inference, simply config the `MODEL_NAME_OR_PATH` and `DATA` in [src/scripts/infer.sh](/src/scripts/infer.sh) and run the following command:

```sh

bash scritps/infer.sh

```

We also open-source the [model outputs](/src/outputs/llm-agents/) from our best models (ToRA-Code-34B and ToRA-70B) in the `src/outputs/` folder.

### ⚖️ Evaluation

The [src/eval/grader.py](/src/eval/) file contains the grading logic that assesses the accuracy of the predicted answer by comparing it to the ground truth. This logic is developed based on the Hendrycks' MATH grading system, which we have manually verified on the MATH dataset to minimize false positives and false negatives.

To evaluate the predicted answer, run the following command:

```sh

python -m eval.evaluate \

    --data_name "math" \

    --prompt_type "tora" \

    --file_path "outputs/llm-agents/tora-code-34b-v1.0/math/test_tora_-1_seed0_t0.0_s0_e5000.jsonl" \

    --execute

```

then you will get:

```

Num samples: 5000

Num scores: 5000

Timeout samples: 0

Empty samples: 2

Mean score: [51.0]

Type scores: {'Algebra': 67.3, 'Counting & Probability': 42.2, 'Geometry': 26.1, 'Intermediate Algebra': 40.0, 'Number Theory': 59.3, 'Prealgebra': 63.8, 'Precalculus': 34.2}

```

### ⚡️ Training

We're currently undergoing an internal review to open-source the ToRA-Corpus-16k, stay tuned!

We also open-source our complete training scripts for the community, and you may construct your own dataset for training. We provide some example data in [data/tora/](/data/tora/).

To train a model, run the following command:

```sh

bash scripts/train.sh codellama 7b

```

## ☕️ Citation

If you find this repository helpful, please consider citing our paper:

```

@inproceedings{

gou2024tora,

title={To{RA}: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving},

author={Zhibin Gou and Zhihong Shao and Yeyun Gong and yelong shen and Yujiu Yang and Minlie Huang and Nan Duan and Weizhu Chen},

booktitle={The Twelfth International Conference on Learning Representations},

year={2024},

url={https://openreview.net/forum?id=Ep0TtjVoap}

}

```

## 🍀 Contributing

This project welcomes contributions and suggestions.  Most contributions require you to agree to a

Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us

the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).

For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or

contact [[email protected]](mailto:[email protected]) with any additional questions or comments.

## 🌟 Star History

[![Star History Chart](https://api.star-history.com/svg?repos=microsoft/ToRA&type=Date)](https://star-history.com/#microsoft/ToRA&Date)